Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases. NSCLC is frequently treated with radiation therapy (RT), among other approaches. In the treatment room, RT has been substantially improved in recent years through developments in conformal dosage technology, allowing radiation to be spatially targeted and modulated according to the orientation of the target volume. However, these innovations have not been adequately matched by corresponding advancements in the planning phase, which determines exactly where RT dose will be distributed. This Bioengineering Research Partnership application seeks to enhance the RT planning process through the incorporation of novel imaging data. Radiation therapy for NSCLC faces the twin challenges of (a) matching the spatiality and intensity of dosage to the location, metastatic potential, and radioresistance of malignancy, and (b) constraining dosage to functionally important lung tissue, which can be irreparably damaged by irradiation. Currently, the target volume and functional areas are defined using anatomical images produced via computed tomography (CT), and recently metabolic images derived from positron emission tomography (PET) have begun to be incorporated. However, the pulmonary data available for NSCLC RT planning remains limited. As such, the proposed research applies hyperpolarized magnetic resonance (HP MR) imaging/spectroscopy to the planning process. HP 13C MR is capable of providing metabolic tissue contrasts including lactate production and pH, which can indicate the presence, aggressiveness, and radioresistance of cancer. HP gas MR supplies quantitative data on lung function and microstructure in the presence of NSCLC and irradiation, which can provide a much more detailed reflection of regional gas exchange quality than the use of CT alone. With the ultimate goals of refining and evaluating HP-MR-based RT planning, the proposed project will pursue three aims. First, we will assess the metabolic, functional, and microstructural effect of irradiation on the lung using HP MR parameters. Second, we will induce rat models of NSCLC, which will be imaged with HP MR and subsequently split into cohorts receiving varying levels of RT dose; each cohort will then be imaged again to ascertain correlations between radiation dose, HP MR parameters, and NSCLC outcomes. Finally, using the results of the first two aims, we will create custom RT plans for NSCLC models incorporating HP MR data and longitudinally compare their effectiveness to conventional CT-based plans in terms of their ability to control tumor growth and avoid radiation-induced pulmonary damage.